Predicting the Intention to Donate Blood among Blood Donors Using a Decision Tree Algorithm

Abstract

The blood donation process is essential for health systems. Therefore, the ability to predict donor flow has become relevant for hospitals. Although it is possible to predict this behaviour intention from donor questionnaires, the need to reduce social contact in pandemic settings leads to decreasing the extension of these surveys with the minimum loss of predictivity. In this context, this study aims to predict the intention to give blood again, among donors, based on a limited number of attributes. This research uses data science and learning concepts based on symmetry in a particular classification to predict blood donation intent. We carried out a face-to-face survey of Chilean donors based on the Theory of Planned Behaviour. These data, including control variables, were analysed using the decision tree technique. The results indicate that it is possible to predict the intention to donate blood again with an accuracy of 84.17% and minimal variables. The added scientific value of this article is to propose a more simplified way of measuring a multi-determined social phenomenon, such as the intention to donate blood again and the application of the decision tree technique to achieve this simplification, thereby contributing to the field of data science.